{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a1d1c1b3",
   "metadata": {},
   "source": [
    "# Formation Energy per Atom Prediction for Crystal Materials: End-to-End Inference Example Using Matformer\n",
    "\n",
    "## Model Overview: What is Matformer\n",
    "\n",
    "**Matformer** is a Graph Neural Network (GNN) specifically designed for **crystal materials**. Its core idea is to model crystal structures as an **atom-bond graph**, learning interactions between atoms to predict physicochemical properties of materials.\n",
    "\n",
    "### Key Features\n",
    "\n",
    "- **Graph-based Modeling**:  \n",
    "  Each atom is treated as a node, chemical bonds between atoms as edges, and lattice vectors as global features, forming a **periodic graph network**.\n",
    "\n",
    "- **Multi-scale Attention Mechanism**:  \n",
    "  Attention weights are used to automatically learn which atoms or bonds have greater influence on the target property (e.g., formation energy).\n",
    "\n",
    "- **High Scalability**:  \n",
    "  Supports multiple materials databases (e.g., Materials Project, OQMD) and can predict various properties such as formation energy, band gap, and elastic modulus.\n",
    "\n",
    "> Reference Paper:  \n",
    "> \"Periodic Graph Transformers for Crystal Material Property Prediction\"\n",
    "\n",
    "This notebook demonstrates how to use the Matformer model to perform inference on **formation energy per atom** of crystal materials and visualize the predicted results along with crystal structures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c880be82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "!pip install pymatgen matplotlib ase numpy pydantic==1.10.9 jarvis-tools==2022.9.16 pathos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a7fc7b46",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zdai/miniconda3/envs/mindc_py39/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
      "  setattr(self, word, getattr(machar, word).flat[0])\n",
      "/home/zdai/miniconda3/envs/mindc_py39/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
      "  return self._float_to_str(self.smallest_subnormal)\n",
      "/home/zdai/miniconda3/envs/mindc_py39/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
      "  setattr(self, word, getattr(machar, word).flat[0])\n",
      "/home/zdai/miniconda3/envs/mindc_py39/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
      "  return self._float_to_str(self.smallest_subnormal)\n",
      "[WARNING] ME(2685237:281473561935904,MainProcess):2025-10-28-18:51:27.953.591 [mindspore/context.py:1412] For 'context.set_context', the parameter 'device_target' will be deprecated and removed in a future version. Please use the api mindspore.set_device() instead.\n",
      "[WARNING] ME(2685237:281473561935904,MainProcess):2025-10-28-18:51:27.955.971 [mindspore/context.py:1412] For 'context.set_context', the parameter 'device_id' will be deprecated and removed in a future version. Please use the api mindspore.set_device() instead.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pickle\n",
    "import yaml\n",
    "import numpy as np\n",
    "import mindspore as ms\n",
    "from mindspore import set_seed\n",
    "from mindspore import nn\n",
    "from mindspore.amp import all_finite\n",
    "from mindchemistry.cell.matformer.matformer import Matformer\n",
    "from mindchemistry.cell.matformer.utils import LossRecord, OneCycleLr\n",
    "from mindchemistry.graph.loss import L1LossMask, L2LossMask\n",
    "from mindchemistry.graph.dataloader import DataLoaderBase as DataLoader\n",
    "\n",
    "config_path = \"config.yaml\"\n",
    "with open(config_path, 'r', encoding='utf-8') as stream:\n",
    "    config = yaml.safe_load(stream)\n",
    "\n",
    "device_target = config['train'][\"device\"]  # \"Ascend\"\n",
    "device_id = config['train'][\"device_id\"]\n",
    "ms.set_context(device_target=device_target, device_id=device_id)\n",
    "set_seed(config['train'][\"seed\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dd5afaa",
   "metadata": {},
   "source": [
    "## Dataset Description: `jdft_3d-12-12-2022.json`\n",
    "\n",
    "### Basic Information\n",
    "\n",
    "- **Dataset Name**: `jdft_3d-12-12-2022.json`\n",
    "- **Source**: [JARVIS-DFT](https://jarvis.nist.gov/) (Joint Automated Repository for Various Integrated Simulations - Density Functional Theory)\n",
    "- **Size**: 75,993 3D crystal structures\n",
    "- **Format**: JSON\n",
    "- **Material Identifier**: Unique ID using `jid` (e.g., `JVASP-90856`)\n",
    "\n",
    "---\n",
    "\n",
    "### Data Overview\n",
    "\n",
    "This dataset contains **3D bulk crystal materials** structures and properties computed using Density Functional Theory (DFT). It is suitable for materials discovery, property prediction, and machine learning modeling tasks.\n",
    "\n",
    "---\n",
    "\n",
    "### Key Fields\n",
    "\n",
    "| Field | Type | Description |\n",
    "|-------|------|-------------|\n",
    "| `jid` | str | Unique JARVIS material ID (e.g., `JVASP-90856`) |\n",
    "| `formula` | str | Chemical formula (e.g., `TiCuSiAs`) |\n",
    "| `spg_number` / `spg_symbol` | int / str | Space group number and symbol (e.g., 129, `P4/nmm`) |\n",
    "| `formation_energy_peratom` | float | Formation energy per atom (eV/atom), more negative indicates higher stability |\n",
    "| `optb88vdw_bandgap` | float | Band gap calculated with OptB88vdW functional (eV) |\n",
    "| `mbj_bandgap`, `hse_gap` | float | Band gap from mBJ or HSE06 functional (available for some materials) |\n",
    "| `atoms` | dict | **Core structure field**, including:<br>• `lattice_mat`: lattice matrix (3×3)<br>• `coords`: atomic coordinates<br>• `elements`: list of elements<br>• `cartesian`: whether coordinates are Cartesian (bool) |\n",
    "| `density` | float | Material density (g/cm³) |\n",
    "| `ehull` | float | Energy above convex hull (eV/atom), <0.1 eV/atom considered stable |\n",
    "| `func` | str | DFT functional used (e.g., `OptB88vdW`) |\n",
    "| `dimensionality` | str | Material dimensionality (all 3D-bulk in this dataset) |\n",
    "| `crys` | str | Crystal system (e.g., `tetragonal`, `cubic`) |\n",
    "| `nat` | int | Total number of atoms |\n",
    "| `reference` | str | Corresponding Materials Project ID (e.g., `mp-1080455`) |\n",
    "\n",
    "---\n",
    "\n",
    "### Dataset Construction\n",
    "\n",
    "The main target of this project is **`formation_energy_peratom`**.  \n",
    "\n",
    "- **Input**: Atoms + lattice information → graph structure  \n",
    "- **Output**: Target property (`formation_energy_peratom`)  \n",
    "\n",
    "The model does **not** directly use the properties as input. Instead, the crystal graph is constructed from the `atoms` field. If you want to use a custom dataset, the `atoms` field is mandatory.\n",
    "\n",
    "Minimal required fields for custom datasets:\n",
    "\n",
    "```json\n",
    "[\n",
    "  {\n",
    "    \"jid\": \"EXAMPLE-0001\",\n",
    "    \"nat\": 4,\n",
    "    \"atoms\": {\n",
    "      \"abc\": [3.56693, 3.56693, 9.39708],\n",
    "      \"angles\": [90.0, 90.0, 90.0],\n",
    "      \"cartesian\": true,\n",
    "      \"coords\": [\n",
    "        [2.6751975, 2.6751975, 7.37610175],\n",
    "        [0.8917325, 0.8917325, 2.02097824],\n",
    "        [0.8917325, 2.6751975, 4.69854],\n",
    "        [2.6751975, 0.8917325, 4.69854],\n",
    "        [0.8917325, 2.6751975, 0.0],\n",
    "        [2.6751975, 0.8917325, 0.0],\n",
    "        [2.6751975, 2.6751975, 2.88947956],\n",
    "        [0.8917325, 0.8917325, 6.50760044]\n",
    "      ],\n",
    "      \"elements\": [\"Ti\",\"Ti\",\"Cu\",\"Cu\",\"Si\",\"Si\",\"As\",\"As\"],\n",
    "      \"lattice_mat\": [\n",
    "        [3.566933224304235, 0.0, -0.0],\n",
    "        [0.0, 3.566933224304235, -0.0],\n",
    "        [-0.0, -0.0, 9.397075454186664]\n",
    "      ],\n",
    "      \"props\": [\"\",\"\",\"\",\"\",\"\",\"\",\"\",\"\"]\n",
    "    },\n",
    "    \"formation_energy_peratom\": -1.23\n",
    "  }\n",
    "]\n",
    "```\n",
    "\n",
    "For other prediction tasks, simply replace `formation_energy_peratom` with another target property, e.g., `optb88vdw_bandgap`:\n",
    "\n",
    "```json\n",
    "{\n",
    "  \"jid\": \"CUSTOM-00002\",\n",
    "  \"nat\": 4,\n",
    "  \"atoms\": {...},\n",
    "  \"optb88vdw_bandgap\": 1.73\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4220126d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from data.generate import get_prop_model\n",
    "# mkdir\n",
    "dataset_dir = config['train'][\"dataset_dir\"]\n",
    "ckpt_dir = config['train'][\"ckpt_dir\"]\n",
    "os.makedirs(dataset_dir, exist_ok=True)\n",
    "os.makedirs(ckpt_dir, exist_ok=True)\n",
    "\n",
    "get_prop_model(prop=config['train'][\"props\"], use_lattice=True, dataset_path=config[\"dataset\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1991bcc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model trainable parameters: %s 2786689\n",
      "Loading existing checkpoint from: %s ./ckpt/best_matformer.ckpt\n",
      "Resuming training from epoch: %d 15\n",
      "Training completed. Best model saved to: %s ./ckpt/best_matformer.ckpt\n"
     ]
    }
   ],
   "source": [
    "# Load training data\n",
    "with open(config['dataset']['x_train_path'], 'rb') as f:\n",
    "    x = pickle.load(f)\n",
    "with open(config['dataset']['edge_index_train_path'], 'rb') as f:\n",
    "    edge_index = pickle.load(f)\n",
    "with open(config['dataset']['edge_attr_train_path'], 'rb') as f:\n",
    "    edge_attr = pickle.load(f)\n",
    "with open(config['dataset']['label_train_path'], 'rb') as f:\n",
    "    label = pickle.load(f)\n",
    "\n",
    "# Load validation data\n",
    "with open(config['dataset']['x_val_path'], 'rb') as f:\n",
    "    x_val = pickle.load(f)\n",
    "with open(config['dataset']['edge_index_val_path'], 'rb') as f:\n",
    "    edge_index_val = pickle.load(f)\n",
    "with open(config['dataset']['edge_attr_val_path'], 'rb') as f:\n",
    "    edge_attr_val = pickle.load(f)\n",
    "with open(config['dataset']['label_val_path'], 'rb') as f:\n",
    "    label_val = pickle.load(f)\n",
    "\n",
    "# Initialize model\n",
    "matformer = Matformer(config['model'])\n",
    "model_parameters = filter(lambda p: p.requires_grad, matformer.get_parameters())\n",
    "params = sum(np.prod(p.shape) for p in model_parameters)\n",
    "print(\"Model trainable parameters: %s\", params)\n",
    "\n",
    "# Optimizer configuration (weight decay grouping)\n",
    "no_decay_params = list(filter(lambda x: 'bias' in x.name or 'norm' in x.name or 'bn' in x.name, matformer.trainable_params()))\n",
    "decay_params = list(filter(lambda x: 'bias' not in x.name and 'norm' not in x.name and 'bn' not in x.name, matformer.trainable_params()))\n",
    "group_params = [{'params': decay_params, 'weight_decay': 0.00001},\n",
    "                {'params': no_decay_params, 'weight_decay': 0},\n",
    "                {'order_params': matformer.trainable_params()}]\n",
    "optimizer = nn.AdamWeightDecay(params=group_params, eps=1e-8)\n",
    "optimizer.beta1 = ms.Tensor([0.95], ms.float32)\n",
    "\n",
    "# Loss functions\n",
    "loss_func_mse = L2LossMask(reduction='mean')\n",
    "loss_func_mae = L1LossMask(reduction='mean')\n",
    "\n",
    "# Define forward pass\n",
    "def forward(data_x, data_edge_attr, data_edge_index, data_batch, data_label, node_mask, edge_mask, node_num, batch_size):\n",
    "    pred = matformer(data_x, data_edge_attr, data_edge_index, data_batch, node_mask, edge_mask, node_num)\n",
    "    mseloss = loss_func_mse(pred, data_label, num=batch_size)\n",
    "    return mseloss, pred\n",
    "\n",
    "# Backward pass\n",
    "backward = ms.value_and_grad(forward, None, weights=matformer.trainable_params(), has_aux=True)\n",
    "\n",
    "# Training step\n",
    "def train_step(data_x, data_edge_attr, data_edge_index, data_batch, data_label, node_mask, edge_mask, node_num, batch_size):\n",
    "    (mseloss, pred), grads = backward(data_x, data_edge_attr, data_edge_index, data_batch, data_label, node_mask, edge_mask, node_num, batch_size)\n",
    "    is_finite = all_finite(grads)\n",
    "    if is_finite:\n",
    "        optimizer(grads)\n",
    "    return mseloss, is_finite, pred\n",
    "\n",
    "# Validation forward pass\n",
    "def forward_eval(data_x, data_edge_attr, data_edge_index, data_batch, data_label, node_mask, edge_mask, node_num, batch_size):\n",
    "    pred = matformer(data_x, data_edge_attr, data_edge_index, data_batch, node_mask, edge_mask, node_num)\n",
    "    mseloss = loss_func_mse(pred, data_label, num=batch_size)\n",
    "    maeloss = loss_func_mae(pred, data_label, num=batch_size)\n",
    "    return mseloss, maeloss, pred\n",
    "\n",
    "def eval_step(data_x, data_edge_attr, data_edge_index, data_batch, data_label, node_mask, edge_mask, node_num, batch_size):\n",
    "    return forward_eval(data_x, data_edge_attr, data_edge_index, data_batch, data_label, node_mask, edge_mask, node_num, batch_size)\n",
    "\n",
    "# Create data loaders\n",
    "BATCH_SIZE = config['train']['batch_size']\n",
    "train_loader = DataLoader(BATCH_SIZE, edge_index, node_attr=x, edge_attr=edge_attr, label=label, dynamic_batch_size=True)\n",
    "eval_loader = DataLoader(BATCH_SIZE, edge_index_val, node_attr=x_val, edge_attr=edge_attr_val, label=label_val, dynamic_batch_size=False)\n",
    "\n",
    "# Learning rate scheduler\n",
    "one_cycle_lr_cls = OneCycleLr(steps_per_epoch=int(len(label) / BATCH_SIZE), epochs=config['train'][\"epoch_size\"], max_lr=0.001, optimizer=optimizer)\n",
    "\n",
    "# Check for existing checkpoint\n",
    "if os.path.exists(config['checkpoint']['best_loss_path']):\n",
    "    print(\"Loading existing checkpoint from: %s\", config['checkpoint']['best_loss_path'])\n",
    "    param_dict = ms.load_checkpoint(config['checkpoint']['best_loss_path'])\n",
    "    epoch = int(param_dict[\"epoch\"]) + 1\n",
    "    param_not_load, _ = ms.load_param_into_net(matformer, param_dict)\n",
    "    one_cycle_lr_cls.current_step = int(param_dict[\"current_step\"])\n",
    "    one_cycle_lr_cls.step()\n",
    "    print(\"Resuming training from epoch: %d\", epoch)\n",
    "else:\n",
    "    print(\"Starting new training process\")\n",
    "    epoch = 0\n",
    "\n",
    "# Main training loop\n",
    "BEST_EPOCH_EVAL_MSE_LOSS = float('inf')\n",
    "for epoch in range(epoch, config['train'][\"epoch_size\"]):\n",
    "    # Training\n",
    "    matformer.set_train(True)\n",
    "    train_mseloss_record = LossRecord()\n",
    "    for batch in train_loader:\n",
    "        (x, edge_attr, y, edge_idx, batch_idx, node_mask, edge_mask, node_num, bs) = batch\n",
    "        mseloss, is_finite, _ = train_step(x, edge_attr, edge_idx, batch_idx, y, node_mask, edge_mask, node_num, bs)\n",
    "        train_mseloss_record.update(mseloss)\n",
    "    # Validation\n",
    "    matformer.set_train(False)\n",
    "    eval_mseloss_record = LossRecord()\n",
    "    eval_maeloss_record = LossRecord()\n",
    "    for batch in eval_loader:\n",
    "        (x, edge_attr, y, edge_idx, batch_idx, node_mask, edge_mask, node_num, bs) = batch\n",
    "        mseloss, maeloss, _ = eval_step(x, edge_attr, edge_idx, batch_idx, y, node_mask, edge_mask, node_num, bs)\n",
    "        eval_mseloss_record.update(mseloss)\n",
    "        eval_maeloss_record.update(maeloss)\n",
    "    # Save best model\n",
    "    if eval_mseloss_record.avg < BEST_EPOCH_EVAL_MSE_LOSS:\n",
    "        BEST_EPOCH_EVAL_MSE_LOSS = eval_mseloss_record.avg\n",
    "        state = {\"epoch\": str(epoch), \"current_step\": str(one_cycle_lr_cls.current_step)}\n",
    "        ms.save_checkpoint(matformer, config['checkpoint']['best_loss_path'], append_dict=state)\n",
    "        print(\"Saved best model at epoch %d, MSE: %.6f\", epoch, eval_mseloss_record.avg)\n",
    "\n",
    "    print(f\"Epoch {epoch} | Train MSE: {train_mseloss_record.avg.item():.6f} | Val MSE: {eval_mseloss_record.avg.item():.6f} | Val MAE: {eval_maeloss_record.avg.item():.6f}\")\n",
    "print(\"Training completed. Best model saved to: %s\", config['checkpoint']['best_loss_path'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e531d317",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model has 2786689 parameters.\n",
      "Loading checkpoint from: ./ckpt/best_matformer.ckpt\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Matformer(\n",
       "  (atom_embedding): Dense(input_channels=92, output_channels=128, has_bias=True)\n",
       "  (rbf_0): RBFExpansion()\n",
       "  (rbf_1): Dense(input_channels=128, output_channels=128, has_bias=True)\n",
       "  (rbf_3): Dense(input_channels=128, output_channels=128, has_bias=True)\n",
       "  (att_layers): CellList(\n",
       "    (0): MatformerConv(128, 128, heads=4)\n",
       "    (1): MatformerConv(128, 128, heads=4)\n",
       "    (2): MatformerConv(128, 128, heads=4)\n",
       "    (3): MatformerConv(128, 128, heads=4)\n",
       "    (4): MatformerConv(128, 128, heads=4)\n",
       "  )\n",
       "  (fc): SequentialCell(\n",
       "    (0): Dense(input_channels=128, output_channels=128, has_bias=True)\n",
       "    (1): Silu(\n",
       "      (sigmoid): Sigmoid()\n",
       "    )\n",
       "  )\n",
       "  (fc_out): Dense(input_channels=128, output_channels=1, has_bias=True)\n",
       "  (aggregator_to_global): AggregateNodeToGlobal()\n",
       ")"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print model parameters\n",
    "model_parameters = filter(lambda p: p.requires_grad, matformer.get_parameters())\n",
    "params = sum(np.prod(p.shape) for p in model_parameters)\n",
    "print(f\"The model has {params} parameters.\")\n",
    "\n",
    "# Define loss functions\n",
    "loss_func_mse = L2LossMask(reduction='mean')\n",
    "loss_func_mae = L1LossMask(reduction='mean')\n",
    "\n",
    "# Load checkpoint\n",
    "checkpoint_dir = config['predictor']['checkpoint_path']\n",
    "\n",
    "if not os.path.exists(checkpoint_dir):\n",
    "    raise FileNotFoundError(f\"Checkpoint not found: {checkpoint_dir}\")\n",
    "\n",
    "print(f\"Loading checkpoint from: {checkpoint_dir}\")\n",
    "param_dict = ms.load_checkpoint(checkpoint_dir)\n",
    "param_not_load, _ = ms.load_param_into_net(matformer, param_dict)\n",
    "if param_not_load:\n",
    "    print(f\"Some parameters were not loaded: {param_not_load}\")\n",
    "\n",
    "matformer.set_train(False)  # Switch to inference mode\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1ae6db19",
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward_eval(data_x, data_edge_attr, data_edge_index, data_batch, data_label,\n",
    "                 node_mask, edge_mask, node_num, batch_size):\n",
    "    pred = matformer(data_x, data_edge_attr, data_edge_index, data_batch,\n",
    "                     node_mask, edge_mask, node_num)\n",
    "    mseloss = loss_func_mse(pred, data_label, num=batch_size)\n",
    "    maeloss = loss_func_mae(pred, data_label, num=batch_size)\n",
    "    return mseloss, maeloss, pred\n",
    "\n",
    "eval_step = forward_eval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "472cb866",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting inference...\n",
      "Batch MSE: 0.010505, MAE: 0.077754\n",
      "Batch MSE: 0.024609, MAE: 0.086742\n",
      "Batch MSE: 0.010080, MAE: 0.080369\n",
      "Batch MSE: 0.022601, MAE: 0.089009\n",
      "Batch MSE: 0.007925, MAE: 0.070052\n",
      "Batch MSE: 0.014160, MAE: 0.082587\n",
      "Batch MSE: 0.016853, MAE: 0.092944\n",
      "Batch MSE: 0.018728, MAE: 0.091988\n",
      "Batch MSE: 0.020404, MAE: 0.091715\n",
      "Batch MSE: 0.024503, MAE: 0.093790\n",
      "Batch MSE: 0.012358, MAE: 0.081957\n",
      "Batch MSE: 0.010347, MAE: 0.073943\n",
      "Batch MSE: 0.018509, MAE: 0.096693\n",
      "Batch MSE: 0.020753, MAE: 0.088838\n",
      "Batch MSE: 0.017787, MAE: 0.092738\n",
      "Batch MSE: 0.009415, MAE: 0.066402\n",
      "Batch MSE: 0.015160, MAE: 0.090073\n",
      "Batch MSE: 0.016694, MAE: 0.095565\n",
      "Batch MSE: 0.019055, MAE: 0.091315\n",
      "Batch MSE: 0.018320, MAE: 0.094847\n",
      "Batch MSE: 0.011553, MAE: 0.075918\n",
      "Batch MSE: 0.010902, MAE: 0.075973\n",
      "Batch MSE: 0.013311, MAE: 0.086189\n",
      "Batch MSE: 0.028303, MAE: 0.103867\n",
      "Batch MSE: 0.012552, MAE: 0.083293\n",
      "Batch MSE: 0.013523, MAE: 0.083151\n",
      "Batch MSE: 0.014025, MAE: 0.087813\n",
      "Batch MSE: 0.026256, MAE: 0.106589\n",
      "Batch MSE: 0.014949, MAE: 0.090461\n",
      "Batch MSE: 0.021990, MAE: 0.105725\n",
      "Batch MSE: 0.017654, MAE: 0.094732\n",
      "Batch MSE: 0.012232, MAE: 0.075133\n",
      "Batch MSE: 0.011860, MAE: 0.075468\n",
      "Batch MSE: 0.019445, MAE: 0.099072\n",
      "Batch MSE: 0.014726, MAE: 0.094282\n",
      "Batch MSE: 0.012363, MAE: 0.083458\n",
      "Batch MSE: 0.013907, MAE: 0.088758\n",
      "Batch MSE: 0.009470, MAE: 0.076859\n",
      "Batch MSE: 0.030238, MAE: 0.099636\n",
      "Batch MSE: 0.015831, MAE: 0.088263\n",
      "Batch MSE: 0.014158, MAE: 0.084029\n",
      "Batch MSE: 0.021176, MAE: 0.095844\n",
      "Batch MSE: 0.017212, MAE: 0.083891\n",
      "Batch MSE: 0.022704, MAE: 0.087270\n",
      "Batch MSE: 0.012207, MAE: 0.084191\n",
      "Batch MSE: 0.017888, MAE: 0.099318\n",
      "Batch MSE: 0.011281, MAE: 0.075426\n",
      "Batch MSE: 0.009593, MAE: 0.071945\n",
      "Batch MSE: 0.018494, MAE: 0.100006\n",
      "Batch MSE: 0.015910, MAE: 0.088858\n",
      "Batch MSE: 0.026271, MAE: 0.096779\n",
      "Batch MSE: 0.013485, MAE: 0.081793\n",
      "Batch MSE: 0.036857, MAE: 0.100053\n",
      "Batch MSE: 0.024955, MAE: 0.106949\n",
      "Batch MSE: 0.029392, MAE: 0.105016\n",
      "Batch MSE: 0.017207, MAE: 0.084067\n",
      "Batch MSE: 0.013041, MAE: 0.088232\n",
      "Batch MSE: 0.009124, MAE: 0.071088\n",
      "Batch MSE: 0.016648, MAE: 0.096520\n",
      "Batch MSE: 0.010705, MAE: 0.078344\n",
      "Batch MSE: 0.015705, MAE: 0.092089\n",
      "Batch MSE: 0.017080, MAE: 0.081701\n",
      "Batch MSE: 0.016804, MAE: 0.090601\n",
      "Batch MSE: 0.021463, MAE: 0.102981\n",
      "Batch MSE: 0.013396, MAE: 0.085234\n",
      "Batch MSE: 0.009247, MAE: 0.071863\n",
      "Batch MSE: 0.010928, MAE: 0.073987\n",
      "Batch MSE: 0.014019, MAE: 0.085319\n",
      "Batch MSE: 0.012801, MAE: 0.084077\n",
      "Batch MSE: 0.008455, MAE: 0.069205\n",
      "Batch MSE: 0.008007, MAE: 0.069982\n",
      "Batch MSE: 0.019679, MAE: 0.094231\n",
      "Batch MSE: 0.011412, MAE: 0.076991\n",
      "Batch MSE: 0.014251, MAE: 0.083272\n",
      "Batch MSE: 0.031069, MAE: 0.105526\n",
      "Batch MSE: 0.006062, MAE: 0.057557\n",
      "Batch MSE: 0.015960, MAE: 0.079852\n",
      "Batch MSE: 0.019594, MAE: 0.092695\n",
      "Batch MSE: 0.018305, MAE: 0.087900\n",
      "Batch MSE: 0.014866, MAE: 0.094591\n",
      "Batch MSE: 0.007526, MAE: 0.065757\n",
      "Batch MSE: 0.013350, MAE: 0.081304\n",
      "Batch MSE: 0.012411, MAE: 0.080136\n",
      "Batch MSE: 0.016448, MAE: 0.090207\n",
      "Batch MSE: 0.013310, MAE: 0.089319\n",
      "Batch MSE: 0.041248, MAE: 0.104639\n",
      "Batch MSE: 0.013993, MAE: 0.085245\n",
      "Batch MSE: 0.021085, MAE: 0.097957\n",
      "Batch MSE: 0.010676, MAE: 0.079995\n",
      "Batch MSE: 0.008286, MAE: 0.070758\n",
      "Batch MSE: 0.011022, MAE: 0.077829\n",
      "Batch MSE: 0.019390, MAE: 0.089854\n",
      "Batch MSE: 0.013218, MAE: 0.075826\n",
      "Batch MSE: 0.007506, MAE: 0.065633\n",
      "Batch MSE: 0.018904, MAE: 0.090410\n",
      "Batch MSE: 0.014927, MAE: 0.085813\n",
      "Batch MSE: 0.013434, MAE: 0.080877\n",
      "Batch MSE: 0.025611, MAE: 0.092905\n",
      "Batch MSE: 0.009493, MAE: 0.073115\n",
      "Batch MSE: 0.010034, MAE: 0.079241\n",
      "Batch MSE: 0.007145, MAE: 0.067775\n",
      "Batch MSE: 0.018899, MAE: 0.094980\n",
      "Batch MSE: 0.020265, MAE: 0.092500\n",
      "Batch MSE: 0.008193, MAE: 0.068542\n",
      "Batch MSE: 0.008638, MAE: 0.069068\n",
      "Batch MSE: 0.016694, MAE: 0.086733\n",
      "Batch MSE: 0.014163, MAE: 0.079244\n",
      "Batch MSE: 0.009659, MAE: 0.073214\n",
      "Batch MSE: 0.013677, MAE: 0.085294\n",
      "Batch MSE: 0.009992, MAE: 0.071685\n",
      "Batch MSE: 0.010330, MAE: 0.075669\n",
      "Batch MSE: 0.013915, MAE: 0.079577\n",
      "Batch MSE: 0.022969, MAE: 0.094670\n",
      "Batch MSE: 0.008642, MAE: 0.073072\n",
      "Batch MSE: 0.015050, MAE: 0.090283\n",
      "Batch MSE: 0.026727, MAE: 0.100731\n",
      "Batch MSE: 0.014841, MAE: 0.074973\n",
      "Batch MSE: 0.012587, MAE: 0.079484\n",
      "Inference completed.\n",
      "Average MSE: 0.015810\n",
      "Average MAE: 0.085403\n"
     ]
    }
   ],
   "source": [
    "# Inference\n",
    "# Initialize DataLoader\n",
    "BATCH_SIZE_MAX = config['train']['batch_size']\n",
    "eval_loader = DataLoader(\n",
    "    BATCH_SIZE_MAX,\n",
    "    edge_index_val,\n",
    "    node_attr=x_val,\n",
    "    edge_attr=edge_attr_val,\n",
    "    label=label_val,\n",
    "    dynamic_batch_size=False,\n",
    "    shuffle_dataset=False\n",
    ")\n",
    "\n",
    "# Store all predictions and labels (for analysis/plotting)\n",
    "all_preds = []\n",
    "all_labels = []\n",
    "\n",
    "eval_mseloss_record = LossRecord()\n",
    "eval_maeloss_record = LossRecord()\n",
    "\n",
    "print(\"Starting inference...\")\n",
    "\n",
    "for batch in eval_loader:\n",
    "    (\n",
    "        node_attr_step, edge_attr_step, label_step, edge_index_step,\n",
    "        node_batch_step, node_mask_step, edge_mask_step,\n",
    "        node_num_step, batch_size_step\n",
    "    ) = batch\n",
    "\n",
    "    mseloss_step, maeloss_step, pred_step = eval_step(\n",
    "        node_attr_step, edge_attr_step, edge_index_step, node_batch_step,\n",
    "        label_step, node_mask_step, edge_mask_step, node_num_step, batch_size_step\n",
    "    )\n",
    "\n",
    "    # Accumulate losses\n",
    "    eval_mseloss_record.update(mseloss_step)\n",
    "    eval_maeloss_record.update(maeloss_step)\n",
    "\n",
    "    # Save predictions and ground truth\n",
    "    all_preds.append(pred_step.asnumpy())\n",
    "    all_labels.append(label_step.asnumpy())\n",
    "\n",
    "    print(f\"Batch MSE: {mseloss_step.asnumpy():.6f}, MAE: {maeloss_step.asnumpy():.6f}\")\n",
    "\n",
    "# Concatenate results\n",
    "all_preds = np.concatenate(all_preds, axis=0)\n",
    "all_labels = np.concatenate(all_labels, axis=0)\n",
    "\n",
    "print(\"\\nInference completed.\")\n",
    "print(f\"Average MSE: {eval_mseloss_record.avg:.6f}\")\n",
    "print(f\"Average MAE: {eval_maeloss_record.avg:.6f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "db83f0e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.scatter(all_labels, all_preds, alpha=0.6)\n",
    "plt.plot([all_labels.min(), all_labels.max()], [all_labels.min(), all_labels.max()], 'r--')\n",
    "plt.xlabel('True Value')\n",
    "plt.ylabel('Predicted Value')\n",
    "plt.title('Prediction vs True (Validation Set)')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5d287e3",
   "metadata": {},
   "source": [
    "### Visualizing Crystal Graph Structures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2aa9bc9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset loaded with 75993 records\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import json\n",
    "import random\n",
    "\n",
    "from ase import Atoms\n",
    "from ase.visualize.plot import plot_atoms\n",
    "\n",
    "# Parameters\n",
    "json_path = \"jdft_3d-12-12-2022.json\"  # Path to dataset\n",
    "\n",
    "n = 6  # Number of samples to visualize\n",
    "seed = 42\n",
    "figsize_each = (4, 4)  # Each subplot size in inches\n",
    "\n",
    "# Load dataset\n",
    "with open(json_path, \"r\", encoding=\"utf-8\") as f:\n",
    "    data = json.load(f)\n",
    "total = len(data)\n",
    "print(f\"Dataset loaded with {total} records\")\n",
    "\n",
    "# Randomly select n samples\n",
    "random.seed(seed)\n",
    "indices = random.sample(range(total), k=min(n, total))\n",
    "\n",
    "# Function to build ASE Atoms object from 'atoms' block\n",
    "def build_ase_atoms_from_atoms_block(atoms_block):\n",
    "    lattice = np.array(atoms_block[\"lattice_mat\"], dtype=float)\n",
    "    elements = atoms_block[\"elements\"]\n",
    "    coords = np.array(atoms_block[\"coords\"], dtype=float)\n",
    "    cartesian_flag = bool(atoms_block.get(\"cartesian\", True))\n",
    "\n",
    "    if not cartesian_flag:\n",
    "        coords = np.dot(coords, lattice)\n",
    "\n",
    "    ase_atoms = Atoms(symbols=elements, positions=coords, cell=lattice, pbc=True)\n",
    "    return ase_atoms\n",
    "\n",
    "# Plot grid\n",
    "cols = 3\n",
    "rows = int(np.ceil(n / cols))\n",
    "fig, axes = plt.subplots(rows, cols, figsize=(cols * figsize_each[0], rows * figsize_each[1]))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for ax_idx, idx in enumerate(indices):\n",
    "    sample = data[idx]\n",
    "    jid = sample.get(\"jid\", f\"sample_{idx}\")\n",
    "    formula = sample.get(\"formula\", \"\")\n",
    "    atoms_block = sample[\"atoms\"]\n",
    "\n",
    "    try:\n",
    "        ase_atoms = build_ase_atoms_from_atoms_block(atoms_block)\n",
    "    except Exception as e:\n",
    "        print(f\"⚠️ Error parsing sample {jid}, skipped: {e}\")\n",
    "        axes[ax_idx].axis(\"off\")\n",
    "        continue\n",
    "\n",
    "    plot_atoms(ase_atoms, ax=axes[ax_idx], radii=0.3, rotation=\"30x,30y,0z\")\n",
    "    axes[ax_idx].set_title(f\"{jid}\\n{formula}\", fontsize=8)\n",
    "    axes[ax_idx].set_xticks([])\n",
    "    axes[ax_idx].set_yticks([])\n",
    "\n",
    "# Turn off remaining empty subplots\n",
    "for j in range(len(indices), rows * cols):\n",
    "    axes[j].axis(\"off\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  }
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